Agentic AI that never leaves your infrastructure.
Open-weight models, private vector stores and agent runtimes deployed in your VPC or on-prem. Full control over data, cost and compliance.
What is self-hosted agentic AI?
Self-hosted agentic AI means running large language models, vector stores and agent runtimes entirely inside your own cloud account or data centre, so no customer data leaves your infrastructure. Eazyware deploys open-weight models with private retrieval, SSO, audit logging and data residency controls for regulated industries such as banking, healthcare and government.
| Service line | AI Agents & Automation |
|---|---|
| Engagement | Scoped build with milestones |
| Duration | Quoted after scoping; typically 8–16 weeks |
| Starting price | $31,500 |
| Typical range | $31,500 – $105,000 + infra |
| Deliverables | 5 listed below |
| Delivered from | Bengaluru, India (IST, UK and US East hours) |
| Code ownership | Client owns code, infrastructure, prompts and documentation |
What problem does it solve?
Banks, healthcare, government and regulated enterprises cannot send customer data to a public API. They also cannot afford to sit out the agent era.
How do we approach it?
Private deployments begin with the compliance conversation, because it decides everything downstream: what data classes exist, which may never leave the environment, which may fall back to a public model, and what the audit and residency requirements are. With that settled we benchmark open-weight models on your actual tasks and size the hardware from measured throughput, not parameter counts. The stack, model serving, vector store, agent runtime, identity, logging, is deployed as infrastructure as code inside your VPC or on-prem, hardened with your security team, and then the agents are built on top with the same discipline as any other engagement: scoped tools, evals, shadow mode, staged autonomy.
What do clients use it for?
- Customer-data agents for banks and insurers
- Clinical document assistants inside a hospital network
- Government workflows with data residency rules
- Sovereign AI for defence and critical infrastructure suppliers
Is it the right fit?
Good fit when
- Regulated industries: BFSI, healthcare, government
- Enterprises with data residency or contractual restrictions
- Teams with high inference volume where private is cheaper
Probably not when
- Small workloads where public APIs are cheaper and compliant
- Teams without an infrastructure owner
What do we build?
- Private LLM serving: Llama, Mistral, Qwen and other open-weight models
- On-prem or VPC agent runtime and orchestration
- Private retrieval layer on pgvector, Qdrant or Weaviate
- SSO, RBAC, audit logging and data residency
- Private-first model routing with optional public fallback for non-sensitive tasks
- GPU sizing, quantisation and cost planning
What you get
- Deployed private stack
- Infrastructure as code
- Security documentation
- Runbooks
- Benchmark report versus public models
How does the engagement work?
- 01
Compliance requirements
- 02
Model selection and benchmark
- 03
Infra design
- 04
Deploy
- 05
Agent build
- 06
Hardening
What does good look like?
Agents doing real work with zero data egress, verified rather than asserted. A deployment your platform team can rebuild from code. Benchmarks showing where the open-weight model matches a public one and where it does not, with the design adjusted accordingly. An audit log auditors can query. And a cost model where, at your volumes, running privately is often cheaper than paying per token.
How does it compare?
| Eazyware | Typical agency | In-house hire | |
|---|---|---|---|
| Time to first result | Sprint Zero in 10 days, then a fixed-scope build | 6–12 weeks of discovery before a proposal | 3–6 months to hire, then ramp |
| Pricing model | Fixed scope, milestone billing, INR or USD | Time and materials, open-ended | Salaries, tooling, management overhead |
| AI depth | Multi-model, evals, cost routing, observability as standard | Often a single vendor API and a prompt | Depends entirely on who you can hire |
| Ownership | Client owns code, infra, prompts and docs | Sometimes retained or licensed back | Owned, but concentrated in one or two people |
| After launch | Care Plans with SLA and AI add-on | Change requests at hourly rates | Ongoing headcount whether or not there is work |
Which pitfalls do we design around?
The common mistakes are choosing a model from a leaderboard rather than from your tasks, under-sizing GPUs from optimistic assumptions, treating security as a checkbox after deployment, and building a private stack nobody in the organisation can operate. We benchmark on your workload, size from measurement, involve your security team from the design week, and hand over runbooks and training so the system has an owner.
What do we measure?
Every engagement is instrumented. These are the numbers you see in the dashboard and the monthly report, not claims on a website.
- Zero data egress verified
- Throughput per GPU and cost per 1k tasks
- Eval parity against public models
- Audit log completeness
Which technologies do we use?
- vLLM / Ollama / TGI
- Kubernetes or bare metal
- pgvector / Qdrant
- LangGraph
- Keycloak / SSO
Who does the work?
A platform engineer with GPU and Kubernetes experience, an AI engineer for model evaluation and agents, and a security-minded architect who works with your information security lead.
What do you need to bring?
An infrastructure or platform owner, your data classification and residency requirements, a security contact for the design review, and either cloud GPU quota or on-prem hardware details. Sample tasks and data for the model benchmark.
Frequently asked questions
Are open models good enough?
For most retrieval, extraction and workflow tasks, yes. We prove it with a ProofRun first.
Hardware?
We size it, from a single L4 to multi-GPU nodes, or use your cloud's GPU instances.
Compliance?
DPDP, GDPR, RBI and HIPAA-aligned patterns. We document controls for your auditors.
Where does this fit?
Agentic AI Solutions (self-hosted) is part of our AI Agents & Automation line. Not sure yet? Start with Sprint Zero, a ten-day discovery whose fee is credited to this build. See all pricing or talk to an engineer.